2022
DOI: 10.1093/nar/gkac321
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VRprofile2: detection of antibiotic resistance-associated mobilome in bacterial pathogens

Abstract: VRprofile2 is an updated pipeline that rapidly identifies diverse mobile genetic elements in bacterial genome sequences. Compared with the previous version, three major improvements were made. First, the user-friendly visualization could aid users in investigating the antibiotic resistance gene cassettes in conjunction with various mobile elements in the multiple resistance region with mosaic structure. VRprofile2 could compare the predicted mobile elements to the collected known mobile elements with similar a… Show more

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Cited by 64 publications
(42 citation statements)
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“…However, CHP cannot be retrieved from the bacterial mobile genetic elements database (mobileOG and VRprofile2) by domain or sequence similarity. 31 , 32 For class D β-lactamases, most were identified in the chromosome as an intrinsic resistance determinant, while only a few were found in mobile genetic elements. 26 , 30 The bla RAD-1 gene reported in this study was located chromosomally and distributed widely in R. anatipestifer.…”
Section: Resultsmentioning
confidence: 99%
“…However, CHP cannot be retrieved from the bacterial mobile genetic elements database (mobileOG and VRprofile2) by domain or sequence similarity. 31 , 32 For class D β-lactamases, most were identified in the chromosome as an intrinsic resistance determinant, while only a few were found in mobile genetic elements. 26 , 30 The bla RAD-1 gene reported in this study was located chromosomally and distributed widely in R. anatipestifer.…”
Section: Resultsmentioning
confidence: 99%
“…Moreover, the phage therapeutic suitability was evaluated by PhageLeads, which analyses the phage genome for temperate genetic markers, AMR, and virulence genes. Other tools were used to further test the feasibility of the phage for therapy, including RGI Resistance Gene Identifier (RGI v5.2.1, CARD v3.2.3 ( Alcock et al., 2020 ), DBETH ( Chakraborty et al., 2012 ), and VRprofile2 ( Zankari et al., 2017 ; Bortolaia et al., 2020 ; Wang et al., 2022 ). PhageTerm (Galaxy v1.0.11) was used to predict the genome termini and packaging from sequence reads ( Garneau et al., 2017 ).…”
Section: Methodsmentioning
confidence: 99%
“…The non-redundant gene sequences constructed from metagenomic data were used to identify MGEs. The ORFs were aligned against corresponding databases for annotating plasmids (PLSDB, (Galata et al, 2019)), insertion sequences (ISs, ISfinder (Siguier et al, 2006)), prophages (PHASTER, (Arndt et al, 2016)), transposons (VRprofile2, (Wang et al, 2022)), integrons (INTEGRALL (Moura et al, 2009)), integrative and conjugative elements (ICEs, ICEberg 2.0 (Liu et al, 2019)), and integrative and mobilizable elements (IME, ICEberg 2.0 (Liu et al, 2019)), respectively. Moreover, a manually curated database of MGEs (mobileOG-db, (Brown et al, 2022)) was used to obtain high-quality and functional annotations.…”
Section: Methodsmentioning
confidence: 99%